{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3208d4bf",
   "metadata": {},
   "source": [
    "This notebook reproduces the tables and figures from the paper that describe our results in hallucination mitigation. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d933f2",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8297b7ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "01b2f917",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sacrebleu import CHRF, BLEU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1cc14f53",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.inter_rater import fleiss_kappa, aggregate_raters\n",
    "from scipy.stats import ttest_rel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d0ddd5",
   "metadata": {},
   "source": [
    "# Analysing reranking"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8756e6aa",
   "metadata": {},
   "source": [
    "### Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "45f9844a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../computed_data/diverse-decoding-results.json', 'r') as f:\n",
    "    unpacked = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af06eb9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "smpl = pd.DataFrame(unpacked['data'])\n",
    "smpl_diverse = unpacked['candidates']\n",
    "hypotheses_scores = unpacked['candidate_scores']\n",
    "selections = unpacked['selections']\n",
    "sel_src_nli_raw = unpacked['nli_scores'] \n",
    "sel_comet_raw = unpacked['comet_scores'] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "246cabc9",
   "metadata": {},
   "source": [
    "### Figure 5: Heatmap of hallucination and rescoring methods quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c54ec38",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_renames = {'ALTI_avg_sc': 'ALTI', 'ref_chrf': 'ChrF++'}\n",
    "\n",
    "final_rankers = ['COMET-QE', 'ALTI_avg_sc', 'LASER2', 'LABSE', 'XNLI']\n",
    "final_rankers_names = ['COMET-QE', 'ALTI', 'LASER2', 'LaBSE', 'XNLI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7815fa58",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_dd592_level0_col0\" class=\"col_heading level0 col0\" >LABSE</th>\n",
       "      <th id=\"T_dd592_level0_col1\" class=\"col_heading level0 col1\" >COMET-QE</th>\n",
       "      <th id=\"T_dd592_level0_col2\" class=\"col_heading level0 col2\" >LASER2</th>\n",
       "      <th id=\"T_dd592_level0_col3\" class=\"col_heading level0 col3\" >ALTI_avg_sc</th>\n",
       "      <th id=\"T_dd592_level0_col4\" class=\"col_heading level0 col4\" >XNLI</th>\n",
       "      <th id=\"T_dd592_level0_col5\" class=\"col_heading level0 col5\" >ref_chrf</th>\n",
       "      <th id=\"T_dd592_level0_col6\" class=\"col_heading level0 col6\" >ref</th>\n",
       "      <th id=\"T_dd592_level0_col7\" class=\"col_heading level0 col7\" >first</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_dd592_level0_row0\" class=\"row_heading level0 row0\" >default</th>\n",
       "      <td id=\"T_dd592_row0_col0\" class=\"data row0 col0\" >0.51</td>\n",
       "      <td id=\"T_dd592_row0_col1\" class=\"data row0 col1\" >0.51</td>\n",
       "      <td id=\"T_dd592_row0_col2\" class=\"data row0 col2\" >0.51</td>\n",
       "      <td id=\"T_dd592_row0_col3\" class=\"data row0 col3\" >0.51</td>\n",
       "      <td id=\"T_dd592_row0_col4\" class=\"data row0 col4\" >0.51</td>\n",
       "      <td id=\"T_dd592_row0_col5\" class=\"data row0 col5\" >0.51</td>\n",
       "      <td id=\"T_dd592_row0_col6\" class=\"data row0 col6\" >0.81</td>\n",
       "      <td id=\"T_dd592_row0_col7\" class=\"data row0 col7\" >0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dd592_level0_row1\" class=\"row_heading level0 row1\" >sampling</th>\n",
       "      <td id=\"T_dd592_row1_col0\" class=\"data row1 col0\" >0.57</td>\n",
       "      <td id=\"T_dd592_row1_col1\" class=\"data row1 col1\" >0.47</td>\n",
       "      <td id=\"T_dd592_row1_col2\" class=\"data row1 col2\" >0.47</td>\n",
       "      <td id=\"T_dd592_row1_col3\" class=\"data row1 col3\" >0.36</td>\n",
       "      <td id=\"T_dd592_row1_col4\" class=\"data row1 col4\" >0.64</td>\n",
       "      <td id=\"T_dd592_row1_col5\" class=\"data row1 col5\" >0.44</td>\n",
       "      <td id=\"T_dd592_row1_col6\" class=\"data row1 col6\" >0.81</td>\n",
       "      <td id=\"T_dd592_row1_col7\" class=\"data row1 col7\" >0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dd592_level0_row2\" class=\"row_heading level0 row2\" >sampling_p08</th>\n",
       "      <td id=\"T_dd592_row2_col0\" class=\"data row2 col0\" >0.66</td>\n",
       "      <td id=\"T_dd592_row2_col1\" class=\"data row2 col1\" >0.58</td>\n",
       "      <td id=\"T_dd592_row2_col2\" class=\"data row2 col2\" >0.63</td>\n",
       "      <td id=\"T_dd592_row2_col3\" class=\"data row2 col3\" >0.58</td>\n",
       "      <td id=\"T_dd592_row2_col4\" class=\"data row2 col4\" >0.71</td>\n",
       "      <td id=\"T_dd592_row2_col5\" class=\"data row2 col5\" >0.61</td>\n",
       "      <td id=\"T_dd592_row2_col6\" class=\"data row2 col6\" >0.81</td>\n",
       "      <td id=\"T_dd592_row2_col7\" class=\"data row2 col7\" >0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dd592_level0_row3\" class=\"row_heading level0 row3\" >beam_search</th>\n",
       "      <td id=\"T_dd592_row3_col0\" class=\"data row3 col0\" >0.65</td>\n",
       "      <td id=\"T_dd592_row3_col1\" class=\"data row3 col1\" >0.61</td>\n",
       "      <td id=\"T_dd592_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
       "      <td id=\"T_dd592_row3_col3\" class=\"data row3 col3\" >0.61</td>\n",
       "      <td id=\"T_dd592_row3_col4\" class=\"data row3 col4\" >0.68</td>\n",
       "      <td id=\"T_dd592_row3_col5\" class=\"data row3 col5\" >0.62</td>\n",
       "      <td id=\"T_dd592_row3_col6\" class=\"data row3 col6\" >0.81</td>\n",
       "      <td id=\"T_dd592_row3_col7\" class=\"data row3 col7\" >0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dd592_level0_row4\" class=\"row_heading level0 row4\" >beam_diversity_1</th>\n",
       "      <td id=\"T_dd592_row4_col0\" class=\"data row4 col0\" >0.70</td>\n",
       "      <td id=\"T_dd592_row4_col1\" class=\"data row4 col1\" >0.63</td>\n",
       "      <td id=\"T_dd592_row4_col2\" class=\"data row4 col2\" >0.67</td>\n",
       "      <td id=\"T_dd592_row4_col3\" class=\"data row4 col3\" >0.64</td>\n",
       "      <td id=\"T_dd592_row4_col4\" class=\"data row4 col4\" >0.74</td>\n",
       "      <td id=\"T_dd592_row4_col5\" class=\"data row4 col5\" >0.66</td>\n",
       "      <td id=\"T_dd592_row4_col6\" class=\"data row4 col6\" >0.81</td>\n",
       "      <td id=\"T_dd592_row4_col7\" class=\"data row4 col7\" >0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dd592_level0_row5\" class=\"row_heading level0 row5\" >beam_diversity_3</th>\n",
       "      <td id=\"T_dd592_row5_col0\" class=\"data row5 col0\" >0.72</td>\n",
       "      <td id=\"T_dd592_row5_col1\" class=\"data row5 col1\" >0.65</td>\n",
       "      <td id=\"T_dd592_row5_col2\" class=\"data row5 col2\" >0.68</td>\n",
       "      <td id=\"T_dd592_row5_col3\" class=\"data row5 col3\" >0.63</td>\n",
       "      <td id=\"T_dd592_row5_col4\" class=\"data row5 col4\" >0.75</td>\n",
       "      <td id=\"T_dd592_row5_col5\" class=\"data row5 col5\" >0.66</td>\n",
       "      <td id=\"T_dd592_row5_col6\" class=\"data row5 col6\" >0.81</td>\n",
       "      <td id=\"T_dd592_row5_col7\" class=\"data row5 col7\" >0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dd592_level0_row6\" class=\"row_heading level0 row6\" >beam_diversity_10</th>\n",
       "      <td id=\"T_dd592_row6_col0\" class=\"data row6 col0\" >0.70</td>\n",
       "      <td id=\"T_dd592_row6_col1\" class=\"data row6 col1\" >0.63</td>\n",
       "      <td id=\"T_dd592_row6_col2\" class=\"data row6 col2\" >0.67</td>\n",
       "      <td id=\"T_dd592_row6_col3\" class=\"data row6 col3\" >0.64</td>\n",
       "      <td id=\"T_dd592_row6_col4\" class=\"data row6 col4\" >0.74</td>\n",
       "      <td id=\"T_dd592_row6_col5\" class=\"data row6 col5\" >0.65</td>\n",
       "      <td id=\"T_dd592_row6_col6\" class=\"data row6 col6\" >0.81</td>\n",
       "      <td id=\"T_dd592_row6_col7\" class=\"data row6 col7\" >0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dd592_level0_row7\" class=\"row_heading level0 row7\" >beam_dbs_1</th>\n",
       "      <td id=\"T_dd592_row7_col0\" class=\"data row7 col0\" >0.70</td>\n",
       "      <td id=\"T_dd592_row7_col1\" class=\"data row7 col1\" >0.63</td>\n",
       "      <td id=\"T_dd592_row7_col2\" class=\"data row7 col2\" >0.66</td>\n",
       "      <td id=\"T_dd592_row7_col3\" class=\"data row7 col3\" >0.63</td>\n",
       "      <td id=\"T_dd592_row7_col4\" class=\"data row7 col4\" >0.74</td>\n",
       "      <td id=\"T_dd592_row7_col5\" class=\"data row7 col5\" >0.64</td>\n",
       "      <td id=\"T_dd592_row7_col6\" class=\"data row7 col6\" >0.81</td>\n",
       "      <td id=\"T_dd592_row7_col7\" class=\"data row7 col7\" >0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dd592_level0_row8\" class=\"row_heading level0 row8\" >beam_dbs_3</th>\n",
       "      <td id=\"T_dd592_row8_col0\" class=\"data row8 col0\" >0.69</td>\n",
       "      <td id=\"T_dd592_row8_col1\" class=\"data row8 col1\" >0.57</td>\n",
       "      <td id=\"T_dd592_row8_col2\" class=\"data row8 col2\" >0.65</td>\n",
       "      <td id=\"T_dd592_row8_col3\" class=\"data row8 col3\" >0.62</td>\n",
       "      <td id=\"T_dd592_row8_col4\" class=\"data row8 col4\" >0.74</td>\n",
       "      <td id=\"T_dd592_row8_col5\" class=\"data row8 col5\" >0.62</td>\n",
       "      <td id=\"T_dd592_row8_col6\" class=\"data row8 col6\" >0.81</td>\n",
       "      <td id=\"T_dd592_row8_col7\" class=\"data row8 col7\" >0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dd592_level0_row9\" class=\"row_heading level0 row9\" >beam_dbs_10</th>\n",
       "      <td id=\"T_dd592_row9_col0\" class=\"data row9 col0\" >0.69</td>\n",
       "      <td id=\"T_dd592_row9_col1\" class=\"data row9 col1\" >0.55</td>\n",
       "      <td id=\"T_dd592_row9_col2\" class=\"data row9 col2\" >0.64</td>\n",
       "      <td id=\"T_dd592_row9_col3\" class=\"data row9 col3\" >0.54</td>\n",
       "      <td id=\"T_dd592_row9_col4\" class=\"data row9 col4\" >0.75</td>\n",
       "      <td id=\"T_dd592_row9_col5\" class=\"data row9 col5\" >0.62</td>\n",
       "      <td id=\"T_dd592_row9_col6\" class=\"data row9 col6\" >0.81</td>\n",
       "      <td id=\"T_dd592_row9_col7\" class=\"data row9 col7\" >0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dd592_level0_row10\" class=\"row_heading level0 row10\" >beam_dropout</th>\n",
       "      <td id=\"T_dd592_row10_col0\" class=\"data row10 col0\" >0.85</td>\n",
       "      <td id=\"T_dd592_row10_col1\" class=\"data row10 col1\" >0.77</td>\n",
       "      <td id=\"T_dd592_row10_col2\" class=\"data row10 col2\" >0.82</td>\n",
       "      <td id=\"T_dd592_row10_col3\" class=\"data row10 col3\" >0.80</td>\n",
       "      <td id=\"T_dd592_row10_col4\" class=\"data row10 col4\" >0.88</td>\n",
       "      <td id=\"T_dd592_row10_col5\" class=\"data row10 col5\" >0.81</td>\n",
       "      <td id=\"T_dd592_row10_col6\" class=\"data row10 col6\" >0.81</td>\n",
       "      <td id=\"T_dd592_row10_col7\" class=\"data row10 col7\" >0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dd592_level0_row11\" class=\"row_heading level0 row11\" >greedy_dropout</th>\n",
       "      <td id=\"T_dd592_row11_col0\" class=\"data row11 col0\" >0.72</td>\n",
       "      <td id=\"T_dd592_row11_col1\" class=\"data row11 col1\" >0.65</td>\n",
       "      <td id=\"T_dd592_row11_col2\" class=\"data row11 col2\" >0.70</td>\n",
       "      <td id=\"T_dd592_row11_col3\" class=\"data row11 col3\" >0.66</td>\n",
       "      <td id=\"T_dd592_row11_col4\" class=\"data row11 col4\" >0.77</td>\n",
       "      <td id=\"T_dd592_row11_col5\" class=\"data row11 col5\" >0.67</td>\n",
       "      <td id=\"T_dd592_row11_col6\" class=\"data row11 col6\" >0.81</td>\n",
       "      <td id=\"T_dd592_row11_col7\" class=\"data row11 col7\" >0.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fee6ad56d30>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_src_nli = {k1: {k2: np.mean(v2) for k2, v2 in v1.items()} for k1, v1 in sel_src_nli_raw.items()}\n",
    "map_xnli = pd.DataFrame(sel_src_nli)\n",
    "map_xnli.style.background_gradient(axis=None).format(precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "258d1fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "  background-color: #88b1d4;\n",
       "  color: #000000;\n",
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       "#T_41ce1_row1_col0 {\n",
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       "  background-color: #acc0dd;\n",
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       "  background-color: #a1bbda;\n",
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       "#T_41ce1_row4_col4, #T_41ce1_row5_col5 {\n",
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       "#T_41ce1_row5_col0 {\n",
       "  background-color: #04629a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row5_col1 {\n",
       "  background-color: #2182b9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row5_col4, #T_41ce1_row6_col5 {\n",
       "  background-color: #197db7;\n",
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       "#T_41ce1_row6_col0 {\n",
       "  background-color: #04649d;\n",
       "  color: #f1f1f1;\n",
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       "#T_41ce1_row6_col1, #T_41ce1_row7_col4 {\n",
       "  background-color: #2987bc;\n",
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       "  background-color: #2685bb;\n",
       "  color: #f1f1f1;\n",
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       "#T_41ce1_row7_col3 {\n",
       "  background-color: #4697c4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row7_col5, #T_41ce1_row8_col2, #T_41ce1_row9_col2, #T_41ce1_row11_col3 {\n",
       "  background-color: #2786bb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row7_col6 {\n",
       "  background-color: #529bc7;\n",
       "  color: #f1f1f1;\n",
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       "#T_41ce1_row8_col1 {\n",
       "  background-color: #4c99c5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row8_col3 {\n",
       "  background-color: #509ac6;\n",
       "  color: #f1f1f1;\n",
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       "#T_41ce1_row8_col4 {\n",
       "  background-color: #2a88bc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row8_col6 {\n",
       "  background-color: #549cc7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row9_col0 {\n",
       "  background-color: #0567a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row9_col1 {\n",
       "  background-color: #6da6cd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row9_col3 {\n",
       "  background-color: #75a9cf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row9_col4 {\n",
       "  background-color: #3790c0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row9_col5 {\n",
       "  background-color: #3f93c2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row10_col0 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row10_col1 {\n",
       "  background-color: #04598c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row10_col2 {\n",
       "  background-color: #034e7b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row10_col3 {\n",
       "  background-color: #045b8f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row10_col4 {\n",
       "  background-color: #034b76;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row10_col5 {\n",
       "  background-color: #034f7d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row11_col0 {\n",
       "  background-color: #045f95;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row11_col1 {\n",
       "  background-color: #1e80b8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row11_col2 {\n",
       "  background-color: #0570b0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row11_col4 {\n",
       "  background-color: #0771b1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row11_col5 {\n",
       "  background-color: #1077b4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_41ce1_row11_col6 {\n",
       "  background-color: #81aed2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_41ce1\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_41ce1_level0_col0\" class=\"col_heading level0 col0\" >LABSE</th>\n",
       "      <th id=\"T_41ce1_level0_col1\" class=\"col_heading level0 col1\" >COMET-QE</th>\n",
       "      <th id=\"T_41ce1_level0_col2\" class=\"col_heading level0 col2\" >LASER2</th>\n",
       "      <th id=\"T_41ce1_level0_col3\" class=\"col_heading level0 col3\" >ALTI_avg_sc</th>\n",
       "      <th id=\"T_41ce1_level0_col4\" class=\"col_heading level0 col4\" >XNLI</th>\n",
       "      <th id=\"T_41ce1_level0_col5\" class=\"col_heading level0 col5\" >ref_chrf</th>\n",
       "      <th id=\"T_41ce1_level0_col6\" class=\"col_heading level0 col6\" >first</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_41ce1_level0_row0\" class=\"row_heading level0 row0\" >default</th>\n",
       "      <td id=\"T_41ce1_row0_col0\" class=\"data row0 col0\" >0.65</td>\n",
       "      <td id=\"T_41ce1_row0_col1\" class=\"data row0 col1\" >0.65</td>\n",
       "      <td id=\"T_41ce1_row0_col2\" class=\"data row0 col2\" >0.65</td>\n",
       "      <td id=\"T_41ce1_row0_col3\" class=\"data row0 col3\" >0.65</td>\n",
       "      <td id=\"T_41ce1_row0_col4\" class=\"data row0 col4\" >0.65</td>\n",
       "      <td id=\"T_41ce1_row0_col5\" class=\"data row0 col5\" >0.65</td>\n",
       "      <td id=\"T_41ce1_row0_col6\" class=\"data row0 col6\" >0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_41ce1_level0_row1\" class=\"row_heading level0 row1\" >sampling</th>\n",
       "      <td id=\"T_41ce1_row1_col0\" class=\"data row1 col0\" >0.70</td>\n",
       "      <td id=\"T_41ce1_row1_col1\" class=\"data row1 col1\" >0.61</td>\n",
       "      <td id=\"T_41ce1_row1_col2\" class=\"data row1 col2\" >0.62</td>\n",
       "      <td id=\"T_41ce1_row1_col3\" class=\"data row1 col3\" >0.54</td>\n",
       "      <td id=\"T_41ce1_row1_col4\" class=\"data row1 col4\" >0.62</td>\n",
       "      <td id=\"T_41ce1_row1_col5\" class=\"data row1 col5\" >0.61</td>\n",
       "      <td id=\"T_41ce1_row1_col6\" class=\"data row1 col6\" >0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_41ce1_level0_row2\" class=\"row_heading level0 row2\" >sampling_p08</th>\n",
       "      <td id=\"T_41ce1_row2_col0\" class=\"data row2 col0\" >0.76</td>\n",
       "      <td id=\"T_41ce1_row2_col1\" class=\"data row2 col1\" >0.68</td>\n",
       "      <td id=\"T_41ce1_row2_col2\" class=\"data row2 col2\" >0.72</td>\n",
       "      <td id=\"T_41ce1_row2_col3\" class=\"data row2 col3\" >0.67</td>\n",
       "      <td id=\"T_41ce1_row2_col4\" class=\"data row2 col4\" >0.71</td>\n",
       "      <td id=\"T_41ce1_row2_col5\" class=\"data row2 col5\" >0.70</td>\n",
       "      <td id=\"T_41ce1_row2_col6\" class=\"data row2 col6\" >0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_41ce1_level0_row3\" class=\"row_heading level0 row3\" >beam_search</th>\n",
       "      <td id=\"T_41ce1_row3_col0\" class=\"data row3 col0\" >0.75</td>\n",
       "      <td id=\"T_41ce1_row3_col1\" class=\"data row3 col1\" >0.71</td>\n",
       "      <td id=\"T_41ce1_row3_col2\" class=\"data row3 col2\" >0.72</td>\n",
       "      <td id=\"T_41ce1_row3_col3\" class=\"data row3 col3\" >0.70</td>\n",
       "      <td id=\"T_41ce1_row3_col4\" class=\"data row3 col4\" >0.71</td>\n",
       "      <td id=\"T_41ce1_row3_col5\" class=\"data row3 col5\" >0.71</td>\n",
       "      <td id=\"T_41ce1_row3_col6\" class=\"data row3 col6\" >0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_41ce1_level0_row4\" class=\"row_heading level0 row4\" >beam_diversity_1</th>\n",
       "      <td id=\"T_41ce1_row4_col0\" class=\"data row4 col0\" >0.78</td>\n",
       "      <td id=\"T_41ce1_row4_col1\" class=\"data row4 col1\" >0.74</td>\n",
       "      <td id=\"T_41ce1_row4_col2\" class=\"data row4 col2\" >0.75</td>\n",
       "      <td id=\"T_41ce1_row4_col3\" class=\"data row4 col3\" >0.72</td>\n",
       "      <td id=\"T_41ce1_row4_col4\" class=\"data row4 col4\" >0.74</td>\n",
       "      <td id=\"T_41ce1_row4_col5\" class=\"data row4 col5\" >0.74</td>\n",
       "      <td id=\"T_41ce1_row4_col6\" class=\"data row4 col6\" >0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_41ce1_level0_row5\" class=\"row_heading level0 row5\" >beam_diversity_3</th>\n",
       "      <td id=\"T_41ce1_row5_col0\" class=\"data row5 col0\" >0.79</td>\n",
       "      <td id=\"T_41ce1_row5_col1\" class=\"data row5 col1\" >0.73</td>\n",
       "      <td id=\"T_41ce1_row5_col2\" class=\"data row5 col2\" >0.74</td>\n",
       "      <td id=\"T_41ce1_row5_col3\" class=\"data row5 col3\" >0.72</td>\n",
       "      <td id=\"T_41ce1_row5_col4\" class=\"data row5 col4\" >0.74</td>\n",
       "      <td id=\"T_41ce1_row5_col5\" class=\"data row5 col5\" >0.74</td>\n",
       "      <td id=\"T_41ce1_row5_col6\" class=\"data row5 col6\" >0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_41ce1_level0_row6\" class=\"row_heading level0 row6\" >beam_diversity_10</th>\n",
       "      <td id=\"T_41ce1_row6_col0\" class=\"data row6 col0\" >0.78</td>\n",
       "      <td id=\"T_41ce1_row6_col1\" class=\"data row6 col1\" >0.72</td>\n",
       "      <td id=\"T_41ce1_row6_col2\" class=\"data row6 col2\" >0.73</td>\n",
       "      <td id=\"T_41ce1_row6_col3\" class=\"data row6 col3\" >0.72</td>\n",
       "      <td id=\"T_41ce1_row6_col4\" class=\"data row6 col4\" >0.73</td>\n",
       "      <td id=\"T_41ce1_row6_col5\" class=\"data row6 col5\" >0.74</td>\n",
       "      <td id=\"T_41ce1_row6_col6\" class=\"data row6 col6\" >0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_41ce1_level0_row7\" class=\"row_heading level0 row7\" >beam_dbs_1</th>\n",
       "      <td id=\"T_41ce1_row7_col0\" class=\"data row7 col0\" >0.78</td>\n",
       "      <td id=\"T_41ce1_row7_col1\" class=\"data row7 col1\" >0.71</td>\n",
       "      <td id=\"T_41ce1_row7_col2\" class=\"data row7 col2\" >0.73</td>\n",
       "      <td id=\"T_41ce1_row7_col3\" class=\"data row7 col3\" >0.70</td>\n",
       "      <td id=\"T_41ce1_row7_col4\" class=\"data row7 col4\" >0.72</td>\n",
       "      <td id=\"T_41ce1_row7_col5\" class=\"data row7 col5\" >0.73</td>\n",
       "      <td id=\"T_41ce1_row7_col6\" class=\"data row7 col6\" >0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_41ce1_level0_row8\" class=\"row_heading level0 row8\" >beam_dbs_3</th>\n",
       "      <td id=\"T_41ce1_row8_col0\" class=\"data row8 col0\" >0.78</td>\n",
       "      <td id=\"T_41ce1_row8_col1\" class=\"data row8 col1\" >0.69</td>\n",
       "      <td id=\"T_41ce1_row8_col2\" class=\"data row8 col2\" >0.72</td>\n",
       "      <td id=\"T_41ce1_row8_col3\" class=\"data row8 col3\" >0.69</td>\n",
       "      <td id=\"T_41ce1_row8_col4\" class=\"data row8 col4\" >0.72</td>\n",
       "      <td id=\"T_41ce1_row8_col5\" class=\"data row8 col5\" >0.71</td>\n",
       "      <td id=\"T_41ce1_row8_col6\" class=\"data row8 col6\" >0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_41ce1_level0_row9\" class=\"row_heading level0 row9\" >beam_dbs_10</th>\n",
       "      <td id=\"T_41ce1_row9_col0\" class=\"data row9 col0\" >0.78</td>\n",
       "      <td id=\"T_41ce1_row9_col1\" class=\"data row9 col1\" >0.67</td>\n",
       "      <td id=\"T_41ce1_row9_col2\" class=\"data row9 col2\" >0.73</td>\n",
       "      <td id=\"T_41ce1_row9_col3\" class=\"data row9 col3\" >0.66</td>\n",
       "      <td id=\"T_41ce1_row9_col4\" class=\"data row9 col4\" >0.71</td>\n",
       "      <td id=\"T_41ce1_row9_col5\" class=\"data row9 col5\" >0.70</td>\n",
       "      <td id=\"T_41ce1_row9_col6\" class=\"data row9 col6\" >0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_41ce1_level0_row10\" class=\"row_heading level0 row10\" >beam_dropout</th>\n",
       "      <td id=\"T_41ce1_row10_col0\" class=\"data row10 col0\" >0.85</td>\n",
       "      <td id=\"T_41ce1_row10_col1\" class=\"data row10 col1\" >0.81</td>\n",
       "      <td id=\"T_41ce1_row10_col2\" class=\"data row10 col2\" >0.82</td>\n",
       "      <td id=\"T_41ce1_row10_col3\" class=\"data row10 col3\" >0.80</td>\n",
       "      <td id=\"T_41ce1_row10_col4\" class=\"data row10 col4\" >0.82</td>\n",
       "      <td id=\"T_41ce1_row10_col5\" class=\"data row10 col5\" >0.82</td>\n",
       "      <td id=\"T_41ce1_row10_col6\" class=\"data row10 col6\" >0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_41ce1_level0_row11\" class=\"row_heading level0 row11\" >greedy_dropout</th>\n",
       "      <td id=\"T_41ce1_row11_col0\" class=\"data row11 col0\" >0.79</td>\n",
       "      <td id=\"T_41ce1_row11_col1\" class=\"data row11 col1\" >0.73</td>\n",
       "      <td id=\"T_41ce1_row11_col2\" class=\"data row11 col2\" >0.76</td>\n",
       "      <td id=\"T_41ce1_row11_col3\" class=\"data row11 col3\" >0.73</td>\n",
       "      <td id=\"T_41ce1_row11_col4\" class=\"data row11 col4\" >0.76</td>\n",
       "      <td id=\"T_41ce1_row11_col5\" class=\"data row11 col5\" >0.75</td>\n",
       "      <td id=\"T_41ce1_row11_col6\" class=\"data row11 col6\" >0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fefe4dbfca0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair2labse = {\n",
    "    (smpl.src[i], hyp): hypotheses_scores['LABSE'][gen][i][j]\n",
    "    for gen, by_gen in smpl_diverse.items()\n",
    "    for i, hyps in enumerate(by_gen)\n",
    "    for j, hyp in enumerate(hyps)\n",
    "}\n",
    "\n",
    "sel_labse = {\n",
    "    scorer: {\n",
    "        generator: np.mean([pair2labse[(smpl.src[i], mt)] for i, mt in enumerate(translations)])\n",
    "        for generator, translations in by_scorer.items()\n",
    "    } \n",
    "    for scorer, by_scorer in selections.items() if scorer != 'ref'\n",
    "}\n",
    "\n",
    "map_labse = pd.DataFrame(sel_labse)\n",
    "map_labse.style.background_gradient(axis=None).format(precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "22b1cf9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "#T_32420_row10_col0 {\n",
       "  background-color: #75a9cf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_32420_row10_col1 {\n",
       "  background-color: #78abd0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_32420_row10_col2 {\n",
       "  background-color: #86b0d3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_32420_row10_col3 {\n",
       "  background-color: #8cb3d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_32420_row10_col4 {\n",
       "  background-color: #80aed2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_32420_row10_col5 {\n",
       "  background-color: #67a4cc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_32420_row11_col5 {\n",
       "  background-color: #94b6d7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_32420_row11_col7 {\n",
       "  background-color: #ced0e6;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_32420\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_32420_level0_col0\" class=\"col_heading level0 col0\" >LABSE</th>\n",
       "      <th id=\"T_32420_level0_col1\" class=\"col_heading level0 col1\" >COMET-QE</th>\n",
       "      <th id=\"T_32420_level0_col2\" class=\"col_heading level0 col2\" >LASER2</th>\n",
       "      <th id=\"T_32420_level0_col3\" class=\"col_heading level0 col3\" >ALTI_avg_sc</th>\n",
       "      <th id=\"T_32420_level0_col4\" class=\"col_heading level0 col4\" >XNLI</th>\n",
       "      <th id=\"T_32420_level0_col5\" class=\"col_heading level0 col5\" >ref_chrf</th>\n",
       "      <th id=\"T_32420_level0_col6\" class=\"col_heading level0 col6\" >ref</th>\n",
       "      <th id=\"T_32420_level0_col7\" class=\"col_heading level0 col7\" >first</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_32420_level0_row0\" class=\"row_heading level0 row0\" >default</th>\n",
       "      <td id=\"T_32420_row0_col0\" class=\"data row0 col0\" >-0.63</td>\n",
       "      <td id=\"T_32420_row0_col1\" class=\"data row0 col1\" >-0.63</td>\n",
       "      <td id=\"T_32420_row0_col2\" class=\"data row0 col2\" >-0.63</td>\n",
       "      <td id=\"T_32420_row0_col3\" class=\"data row0 col3\" >-0.63</td>\n",
       "      <td id=\"T_32420_row0_col4\" class=\"data row0 col4\" >-0.63</td>\n",
       "      <td id=\"T_32420_row0_col5\" class=\"data row0 col5\" >-0.63</td>\n",
       "      <td id=\"T_32420_row0_col6\" class=\"data row0 col6\" >1.04</td>\n",
       "      <td id=\"T_32420_row0_col7\" class=\"data row0 col7\" >-0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32420_level0_row1\" class=\"row_heading level0 row1\" >sampling</th>\n",
       "      <td id=\"T_32420_row1_col0\" class=\"data row1 col0\" >-0.63</td>\n",
       "      <td id=\"T_32420_row1_col1\" class=\"data row1 col1\" >-0.57</td>\n",
       "      <td id=\"T_32420_row1_col2\" class=\"data row1 col2\" >-0.83</td>\n",
       "      <td id=\"T_32420_row1_col3\" class=\"data row1 col3\" >-1.07</td>\n",
       "      <td id=\"T_32420_row1_col4\" class=\"data row1 col4\" >-0.77</td>\n",
       "      <td id=\"T_32420_row1_col5\" class=\"data row1 col5\" >-0.66</td>\n",
       "      <td id=\"T_32420_row1_col6\" class=\"data row1 col6\" >1.04</td>\n",
       "      <td id=\"T_32420_row1_col7\" class=\"data row1 col7\" >-1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32420_level0_row2\" class=\"row_heading level0 row2\" >sampling_p08</th>\n",
       "      <td id=\"T_32420_row2_col0\" class=\"data row2 col0\" >-0.30</td>\n",
       "      <td id=\"T_32420_row2_col1\" class=\"data row2 col1\" >-0.29</td>\n",
       "      <td id=\"T_32420_row2_col2\" class=\"data row2 col2\" >-0.39</td>\n",
       "      <td id=\"T_32420_row2_col3\" class=\"data row2 col3\" >-0.48</td>\n",
       "      <td id=\"T_32420_row2_col4\" class=\"data row2 col4\" >-0.39</td>\n",
       "      <td id=\"T_32420_row2_col5\" class=\"data row2 col5\" >-0.30</td>\n",
       "      <td id=\"T_32420_row2_col6\" class=\"data row2 col6\" >1.04</td>\n",
       "      <td id=\"T_32420_row2_col7\" class=\"data row2 col7\" >-0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32420_level0_row3\" class=\"row_heading level0 row3\" >beam_search</th>\n",
       "      <td id=\"T_32420_row3_col0\" class=\"data row3 col0\" >-0.35</td>\n",
       "      <td id=\"T_32420_row3_col1\" class=\"data row3 col1\" >-0.35</td>\n",
       "      <td id=\"T_32420_row3_col2\" class=\"data row3 col2\" >-0.42</td>\n",
       "      <td id=\"T_32420_row3_col3\" class=\"data row3 col3\" >-0.46</td>\n",
       "      <td id=\"T_32420_row3_col4\" class=\"data row3 col4\" >-0.43</td>\n",
       "      <td id=\"T_32420_row3_col5\" class=\"data row3 col5\" >-0.29</td>\n",
       "      <td id=\"T_32420_row3_col6\" class=\"data row3 col6\" >1.04</td>\n",
       "      <td id=\"T_32420_row3_col7\" class=\"data row3 col7\" >-0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32420_level0_row4\" class=\"row_heading level0 row4\" >beam_diversity_1</th>\n",
       "      <td id=\"T_32420_row4_col0\" class=\"data row4 col0\" >-0.27</td>\n",
       "      <td id=\"T_32420_row4_col1\" class=\"data row4 col1\" >-0.28</td>\n",
       "      <td id=\"T_32420_row4_col2\" class=\"data row4 col2\" >-0.34</td>\n",
       "      <td id=\"T_32420_row4_col3\" class=\"data row4 col3\" >-0.40</td>\n",
       "      <td id=\"T_32420_row4_col4\" class=\"data row4 col4\" >-0.35</td>\n",
       "      <td id=\"T_32420_row4_col5\" class=\"data row4 col5\" >-0.19</td>\n",
       "      <td id=\"T_32420_row4_col6\" class=\"data row4 col6\" >1.04</td>\n",
       "      <td id=\"T_32420_row4_col7\" class=\"data row4 col7\" >-0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32420_level0_row5\" class=\"row_heading level0 row5\" >beam_diversity_3</th>\n",
       "      <td id=\"T_32420_row5_col0\" class=\"data row5 col0\" >-0.26</td>\n",
       "      <td id=\"T_32420_row5_col1\" class=\"data row5 col1\" >-0.28</td>\n",
       "      <td id=\"T_32420_row5_col2\" class=\"data row5 col2\" >-0.37</td>\n",
       "      <td id=\"T_32420_row5_col3\" class=\"data row5 col3\" >-0.41</td>\n",
       "      <td id=\"T_32420_row5_col4\" class=\"data row5 col4\" >-0.36</td>\n",
       "      <td id=\"T_32420_row5_col5\" class=\"data row5 col5\" >-0.22</td>\n",
       "      <td id=\"T_32420_row5_col6\" class=\"data row5 col6\" >1.04</td>\n",
       "      <td id=\"T_32420_row5_col7\" class=\"data row5 col7\" >-0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32420_level0_row6\" class=\"row_heading level0 row6\" >beam_diversity_10</th>\n",
       "      <td id=\"T_32420_row6_col0\" class=\"data row6 col0\" >-0.28</td>\n",
       "      <td id=\"T_32420_row6_col1\" class=\"data row6 col1\" >-0.28</td>\n",
       "      <td id=\"T_32420_row6_col2\" class=\"data row6 col2\" >-0.40</td>\n",
       "      <td id=\"T_32420_row6_col3\" class=\"data row6 col3\" >-0.42</td>\n",
       "      <td id=\"T_32420_row6_col4\" class=\"data row6 col4\" >-0.40</td>\n",
       "      <td id=\"T_32420_row6_col5\" class=\"data row6 col5\" >-0.22</td>\n",
       "      <td id=\"T_32420_row6_col6\" class=\"data row6 col6\" >1.04</td>\n",
       "      <td id=\"T_32420_row6_col7\" class=\"data row6 col7\" >-0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32420_level0_row7\" class=\"row_heading level0 row7\" >beam_dbs_1</th>\n",
       "      <td id=\"T_32420_row7_col0\" class=\"data row7 col0\" >-0.25</td>\n",
       "      <td id=\"T_32420_row7_col1\" class=\"data row7 col1\" >-0.25</td>\n",
       "      <td id=\"T_32420_row7_col2\" class=\"data row7 col2\" >-0.36</td>\n",
       "      <td id=\"T_32420_row7_col3\" class=\"data row7 col3\" >-0.42</td>\n",
       "      <td id=\"T_32420_row7_col4\" class=\"data row7 col4\" >-0.36</td>\n",
       "      <td id=\"T_32420_row7_col5\" class=\"data row7 col5\" >-0.22</td>\n",
       "      <td id=\"T_32420_row7_col6\" class=\"data row7 col6\" >1.04</td>\n",
       "      <td id=\"T_32420_row7_col7\" class=\"data row7 col7\" >-0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32420_level0_row8\" class=\"row_heading level0 row8\" >beam_dbs_3</th>\n",
       "      <td id=\"T_32420_row8_col0\" class=\"data row8 col0\" >-0.27</td>\n",
       "      <td id=\"T_32420_row8_col1\" class=\"data row8 col1\" >-0.29</td>\n",
       "      <td id=\"T_32420_row8_col2\" class=\"data row8 col2\" >-0.37</td>\n",
       "      <td id=\"T_32420_row8_col3\" class=\"data row8 col3\" >-0.44</td>\n",
       "      <td id=\"T_32420_row8_col4\" class=\"data row8 col4\" >-0.38</td>\n",
       "      <td id=\"T_32420_row8_col5\" class=\"data row8 col5\" >-0.24</td>\n",
       "      <td id=\"T_32420_row8_col6\" class=\"data row8 col6\" >1.04</td>\n",
       "      <td id=\"T_32420_row8_col7\" class=\"data row8 col7\" >-0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32420_level0_row9\" class=\"row_heading level0 row9\" >beam_dbs_10</th>\n",
       "      <td id=\"T_32420_row9_col0\" class=\"data row9 col0\" >-0.28</td>\n",
       "      <td id=\"T_32420_row9_col1\" class=\"data row9 col1\" >-0.33</td>\n",
       "      <td id=\"T_32420_row9_col2\" class=\"data row9 col2\" >-0.37</td>\n",
       "      <td id=\"T_32420_row9_col3\" class=\"data row9 col3\" >-0.57</td>\n",
       "      <td id=\"T_32420_row9_col4\" class=\"data row9 col4\" >-0.43</td>\n",
       "      <td id=\"T_32420_row9_col5\" class=\"data row9 col5\" >-0.28</td>\n",
       "      <td id=\"T_32420_row9_col6\" class=\"data row9 col6\" >1.04</td>\n",
       "      <td id=\"T_32420_row9_col7\" class=\"data row9 col7\" >-0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32420_level0_row10\" class=\"row_heading level0 row10\" >beam_dropout</th>\n",
       "      <td id=\"T_32420_row10_col0\" class=\"data row10 col0\" >-0.01</td>\n",
       "      <td id=\"T_32420_row10_col1\" class=\"data row10 col1\" >-0.03</td>\n",
       "      <td id=\"T_32420_row10_col2\" class=\"data row10 col2\" >-0.11</td>\n",
       "      <td id=\"T_32420_row10_col3\" class=\"data row10 col3\" >-0.14</td>\n",
       "      <td id=\"T_32420_row10_col4\" class=\"data row10 col4\" >-0.07</td>\n",
       "      <td id=\"T_32420_row10_col5\" class=\"data row10 col5\" >0.05</td>\n",
       "      <td id=\"T_32420_row10_col6\" class=\"data row10 col6\" >1.04</td>\n",
       "      <td id=\"T_32420_row10_col7\" class=\"data row10 col7\" >-0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32420_level0_row11\" class=\"row_heading level0 row11\" >greedy_dropout</th>\n",
       "      <td id=\"T_32420_row11_col0\" class=\"data row11 col0\" >-0.21</td>\n",
       "      <td id=\"T_32420_row11_col1\" class=\"data row11 col1\" >-0.23</td>\n",
       "      <td id=\"T_32420_row11_col2\" class=\"data row11 col2\" >-0.28</td>\n",
       "      <td id=\"T_32420_row11_col3\" class=\"data row11 col3\" >-0.39</td>\n",
       "      <td id=\"T_32420_row11_col4\" class=\"data row11 col4\" >-0.26</td>\n",
       "      <td id=\"T_32420_row11_col5\" class=\"data row11 col5\" >-0.18</td>\n",
       "      <td id=\"T_32420_row11_col6\" class=\"data row11 col6\" >1.04</td>\n",
       "      <td id=\"T_32420_row11_col7\" class=\"data row11 col7\" >-0.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fee68447d90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_comet = {k1: {k2: np.mean(v2) for k2, v2 in v1.items()} for k1, v1 in sel_comet_raw.items()}\n",
    "map_comet = pd.DataFrame(sel_comet)\n",
    "map_comet.style.background_gradient(axis=None).format(precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86f88ea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_a18b1_row0_col0, #T_a18b1_row0_col1, #T_a18b1_row0_col2, #T_a18b1_row0_col3, #T_a18b1_row0_col4, #T_a18b1_row0_col5, #T_a18b1_row0_col7, #T_a18b1_row9_col1 {\n",
       "  background-color: #f5eef6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row0_col6, #T_a18b1_row1_col6, #T_a18b1_row2_col6, #T_a18b1_row3_col6, #T_a18b1_row4_col6, #T_a18b1_row5_col6, #T_a18b1_row6_col6, #T_a18b1_row7_col6, #T_a18b1_row8_col6, #T_a18b1_row9_col6, #T_a18b1_row10_col6, #T_a18b1_row11_col6 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a18b1_row1_col0, #T_a18b1_row2_col7 {\n",
       "  background-color: #f8f1f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row1_col1 {\n",
       "  background-color: #f9f2f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row1_col2 {\n",
       "  background-color: #fbf3f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row1_col3 {\n",
       "  background-color: #fef6fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row1_col4 {\n",
       "  background-color: #faf3f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row1_col5 {\n",
       "  background-color: #f7f0f7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row1_col7 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row2_col0, #T_a18b1_row3_col1, #T_a18b1_row3_col4, #T_a18b1_row4_col3, #T_a18b1_row7_col4, #T_a18b1_row9_col2 {\n",
       "  background-color: #f1ebf4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row2_col1, #T_a18b1_row3_col3 {\n",
       "  background-color: #f3edf5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row2_col2, #T_a18b1_row2_col4, #T_a18b1_row9_col3 {\n",
       "  background-color: #f4edf6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row2_col3 {\n",
       "  background-color: #f6eff7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row2_col5, #T_a18b1_row4_col7, #T_a18b1_row6_col1, #T_a18b1_row7_col0 {\n",
       "  background-color: #ede8f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row3_col0, #T_a18b1_row4_col4, #T_a18b1_row5_col3, #T_a18b1_row6_col7, #T_a18b1_row7_col1, #T_a18b1_row7_col3, #T_a18b1_row8_col0, #T_a18b1_row8_col2, #T_a18b1_row9_col7 {\n",
       "  background-color: #f0eaf4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row3_col2, #T_a18b1_row3_col7 {\n",
       "  background-color: #f1ebf5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row3_col5, #T_a18b1_row8_col5, #T_a18b1_row11_col0, #T_a18b1_row11_col4 {\n",
       "  background-color: #e7e3f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row4_col0 {\n",
       "  background-color: #ede7f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row4_col1, #T_a18b1_row5_col2, #T_a18b1_row5_col7, #T_a18b1_row6_col2 {\n",
       "  background-color: #eee8f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row4_col2, #T_a18b1_row6_col3, #T_a18b1_row6_col4, #T_a18b1_row7_col2, #T_a18b1_row7_col7 {\n",
       "  background-color: #eee9f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row4_col5 {\n",
       "  background-color: #e1dfed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row5_col0, #T_a18b1_row6_col0 {\n",
       "  background-color: #ece7f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row5_col1, #T_a18b1_row5_col4, #T_a18b1_row8_col7, #T_a18b1_row9_col0, #T_a18b1_row11_col3 {\n",
       "  background-color: #efe9f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row5_col5 {\n",
       "  background-color: #e2dfee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row6_col5 {\n",
       "  background-color: #e4e1ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row7_col5 {\n",
       "  background-color: #e3e0ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row8_col1, #T_a18b1_row8_col3, #T_a18b1_row8_col4, #T_a18b1_row11_col7 {\n",
       "  background-color: #f2ecf5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row9_col4 {\n",
       "  background-color: #f4eef6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row9_col5, #T_a18b1_row11_col1 {\n",
       "  background-color: #e9e5f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row10_col0, #T_a18b1_row11_col5 {\n",
       "  background-color: #dcdaeb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row10_col1 {\n",
       "  background-color: #e0dded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row10_col2 {\n",
       "  background-color: #dfddec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row10_col3, #T_a18b1_row10_col4 {\n",
       "  background-color: #e0deed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row10_col5 {\n",
       "  background-color: #d2d2e7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row10_col7 {\n",
       "  background-color: #ebe6f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a18b1_row11_col2 {\n",
       "  background-color: #e6e2ef;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a18b1\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a18b1_level0_col0\" class=\"col_heading level0 col0\" >LABSE</th>\n",
       "      <th id=\"T_a18b1_level0_col1\" class=\"col_heading level0 col1\" >COMET-QE</th>\n",
       "      <th id=\"T_a18b1_level0_col2\" class=\"col_heading level0 col2\" >LASER2</th>\n",
       "      <th id=\"T_a18b1_level0_col3\" class=\"col_heading level0 col3\" >ALTI_avg_sc</th>\n",
       "      <th id=\"T_a18b1_level0_col4\" class=\"col_heading level0 col4\" >XNLI</th>\n",
       "      <th id=\"T_a18b1_level0_col5\" class=\"col_heading level0 col5\" >ref_chrf</th>\n",
       "      <th id=\"T_a18b1_level0_col6\" class=\"col_heading level0 col6\" >ref</th>\n",
       "      <th id=\"T_a18b1_level0_col7\" class=\"col_heading level0 col7\" >first</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a18b1_level0_row0\" class=\"row_heading level0 row0\" >default</th>\n",
       "      <td id=\"T_a18b1_row0_col0\" class=\"data row0 col0\" >12.67</td>\n",
       "      <td id=\"T_a18b1_row0_col1\" class=\"data row0 col1\" >12.67</td>\n",
       "      <td id=\"T_a18b1_row0_col2\" class=\"data row0 col2\" >12.67</td>\n",
       "      <td id=\"T_a18b1_row0_col3\" class=\"data row0 col3\" >12.67</td>\n",
       "      <td id=\"T_a18b1_row0_col4\" class=\"data row0 col4\" >12.67</td>\n",
       "      <td id=\"T_a18b1_row0_col5\" class=\"data row0 col5\" >12.67</td>\n",
       "      <td id=\"T_a18b1_row0_col6\" class=\"data row0 col6\" >100.00</td>\n",
       "      <td id=\"T_a18b1_row0_col7\" class=\"data row0 col7\" >12.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a18b1_level0_row1\" class=\"row_heading level0 row1\" >sampling</th>\n",
       "      <td id=\"T_a18b1_row1_col0\" class=\"data row1 col0\" >11.05</td>\n",
       "      <td id=\"T_a18b1_row1_col1\" class=\"data row1 col1\" >10.36</td>\n",
       "      <td id=\"T_a18b1_row1_col2\" class=\"data row1 col2\" >9.22</td>\n",
       "      <td id=\"T_a18b1_row1_col3\" class=\"data row1 col3\" >6.92</td>\n",
       "      <td id=\"T_a18b1_row1_col4\" class=\"data row1 col4\" >9.44</td>\n",
       "      <td id=\"T_a18b1_row1_col5\" class=\"data row1 col5\" >11.79</td>\n",
       "      <td id=\"T_a18b1_row1_col6\" class=\"data row1 col6\" >100.00</td>\n",
       "      <td id=\"T_a18b1_row1_col7\" class=\"data row1 col7\" >6.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a18b1_level0_row2\" class=\"row_heading level0 row2\" >sampling_p08</th>\n",
       "      <td id=\"T_a18b1_row2_col0\" class=\"data row2 col0\" >15.27</td>\n",
       "      <td id=\"T_a18b1_row2_col1\" class=\"data row2 col1\" >13.97</td>\n",
       "      <td id=\"T_a18b1_row2_col2\" class=\"data row2 col2\" >13.55</td>\n",
       "      <td id=\"T_a18b1_row2_col3\" class=\"data row2 col3\" >12.05</td>\n",
       "      <td id=\"T_a18b1_row2_col4\" class=\"data row2 col4\" >13.55</td>\n",
       "      <td id=\"T_a18b1_row2_col5\" class=\"data row2 col5\" >17.57</td>\n",
       "      <td id=\"T_a18b1_row2_col6\" class=\"data row2 col6\" >100.00</td>\n",
       "      <td id=\"T_a18b1_row2_col7\" class=\"data row2 col7\" >11.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a18b1_level0_row3\" class=\"row_heading level0 row3\" >beam_search</th>\n",
       "      <td id=\"T_a18b1_row3_col0\" class=\"data row3 col0\" >16.30</td>\n",
       "      <td id=\"T_a18b1_row3_col1\" class=\"data row3 col1\" >15.44</td>\n",
       "      <td id=\"T_a18b1_row3_col2\" class=\"data row3 col2\" >15.15</td>\n",
       "      <td id=\"T_a18b1_row3_col3\" class=\"data row3 col3\" >13.94</td>\n",
       "      <td id=\"T_a18b1_row3_col4\" class=\"data row3 col4\" >15.31</td>\n",
       "      <td id=\"T_a18b1_row3_col5\" class=\"data row3 col5\" >20.00</td>\n",
       "      <td id=\"T_a18b1_row3_col6\" class=\"data row3 col6\" >100.00</td>\n",
       "      <td id=\"T_a18b1_row3_col7\" class=\"data row3 col7\" >15.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a18b1_level0_row4\" class=\"row_heading level0 row4\" >beam_diversity_1</th>\n",
       "      <td id=\"T_a18b1_row4_col0\" class=\"data row4 col0\" >17.92</td>\n",
       "      <td id=\"T_a18b1_row4_col1\" class=\"data row4 col1\" >17.21</td>\n",
       "      <td id=\"T_a18b1_row4_col2\" class=\"data row4 col2\" >16.80</td>\n",
       "      <td id=\"T_a18b1_row4_col3\" class=\"data row4 col3\" >15.52</td>\n",
       "      <td id=\"T_a18b1_row4_col4\" class=\"data row4 col4\" >16.28</td>\n",
       "      <td id=\"T_a18b1_row4_col5\" class=\"data row4 col5\" >22.56</td>\n",
       "      <td id=\"T_a18b1_row4_col6\" class=\"data row4 col6\" >100.00</td>\n",
       "      <td id=\"T_a18b1_row4_col7\" class=\"data row4 col7\" >17.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a18b1_level0_row5\" class=\"row_heading level0 row5\" >beam_diversity_3</th>\n",
       "      <td id=\"T_a18b1_row5_col0\" class=\"data row5 col0\" >18.18</td>\n",
       "      <td id=\"T_a18b1_row5_col1\" class=\"data row5 col1\" >16.65</td>\n",
       "      <td id=\"T_a18b1_row5_col2\" class=\"data row5 col2\" >17.06</td>\n",
       "      <td id=\"T_a18b1_row5_col3\" class=\"data row5 col3\" >16.06</td>\n",
       "      <td id=\"T_a18b1_row5_col4\" class=\"data row5 col4\" >16.43</td>\n",
       "      <td id=\"T_a18b1_row5_col5\" class=\"data row5 col5\" >22.44</td>\n",
       "      <td id=\"T_a18b1_row5_col6\" class=\"data row5 col6\" >100.00</td>\n",
       "      <td id=\"T_a18b1_row5_col7\" class=\"data row5 col7\" >17.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a18b1_level0_row6\" class=\"row_heading level0 row6\" >beam_diversity_10</th>\n",
       "      <td id=\"T_a18b1_row6_col0\" class=\"data row6 col0\" >18.25</td>\n",
       "      <td id=\"T_a18b1_row6_col1\" class=\"data row6 col1\" >17.57</td>\n",
       "      <td id=\"T_a18b1_row6_col2\" class=\"data row6 col2\" >17.33</td>\n",
       "      <td id=\"T_a18b1_row6_col3\" class=\"data row6 col3\" >16.95</td>\n",
       "      <td id=\"T_a18b1_row6_col4\" class=\"data row6 col4\" >17.04</td>\n",
       "      <td id=\"T_a18b1_row6_col5\" class=\"data row6 col5\" >21.52</td>\n",
       "      <td id=\"T_a18b1_row6_col6\" class=\"data row6 col6\" >100.00</td>\n",
       "      <td id=\"T_a18b1_row6_col7\" class=\"data row6 col7\" >15.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a18b1_level0_row7\" class=\"row_heading level0 row7\" >beam_dbs_1</th>\n",
       "      <td id=\"T_a18b1_row7_col0\" class=\"data row7 col0\" >17.53</td>\n",
       "      <td id=\"T_a18b1_row7_col1\" class=\"data row7 col1\" >15.99</td>\n",
       "      <td id=\"T_a18b1_row7_col2\" class=\"data row7 col2\" >16.93</td>\n",
       "      <td id=\"T_a18b1_row7_col3\" class=\"data row7 col3\" >16.21</td>\n",
       "      <td id=\"T_a18b1_row7_col4\" class=\"data row7 col4\" >15.55</td>\n",
       "      <td id=\"T_a18b1_row7_col5\" class=\"data row7 col5\" >21.91</td>\n",
       "      <td id=\"T_a18b1_row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
       "      <td id=\"T_a18b1_row7_col7\" class=\"data row7 col7\" >16.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a18b1_level0_row8\" class=\"row_heading level0 row8\" >beam_dbs_3</th>\n",
       "      <td id=\"T_a18b1_row8_col0\" class=\"data row8 col0\" >16.14</td>\n",
       "      <td id=\"T_a18b1_row8_col1\" class=\"data row8 col1\" >14.32</td>\n",
       "      <td id=\"T_a18b1_row8_col2\" class=\"data row8 col2\" >15.63</td>\n",
       "      <td id=\"T_a18b1_row8_col3\" class=\"data row8 col3\" >14.64</td>\n",
       "      <td id=\"T_a18b1_row8_col4\" class=\"data row8 col4\" >14.39</td>\n",
       "      <td id=\"T_a18b1_row8_col5\" class=\"data row8 col5\" >20.22</td>\n",
       "      <td id=\"T_a18b1_row8_col6\" class=\"data row8 col6\" >100.00</td>\n",
       "      <td id=\"T_a18b1_row8_col7\" class=\"data row8 col7\" >16.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a18b1_level0_row9\" class=\"row_heading level0 row9\" >beam_dbs_10</th>\n",
       "      <td id=\"T_a18b1_row9_col0\" class=\"data row9 col0\" >16.52</td>\n",
       "      <td id=\"T_a18b1_row9_col1\" class=\"data row9 col1\" >12.76</td>\n",
       "      <td id=\"T_a18b1_row9_col2\" class=\"data row9 col2\" >15.34</td>\n",
       "      <td id=\"T_a18b1_row9_col3\" class=\"data row9 col3\" >13.69</td>\n",
       "      <td id=\"T_a18b1_row9_col4\" class=\"data row9 col4\" >13.04</td>\n",
       "      <td id=\"T_a18b1_row9_col5\" class=\"data row9 col5\" >19.53</td>\n",
       "      <td id=\"T_a18b1_row9_col6\" class=\"data row9 col6\" >100.00</td>\n",
       "      <td id=\"T_a18b1_row9_col7\" class=\"data row9 col7\" >15.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a18b1_level0_row10\" class=\"row_heading level0 row10\" >beam_dropout</th>\n",
       "      <td id=\"T_a18b1_row10_col0\" class=\"data row10 col0\" >24.90</td>\n",
       "      <td id=\"T_a18b1_row10_col1\" class=\"data row10 col1\" >23.55</td>\n",
       "      <td id=\"T_a18b1_row10_col2\" class=\"data row10 col2\" >23.69</td>\n",
       "      <td id=\"T_a18b1_row10_col3\" class=\"data row10 col3\" >23.14</td>\n",
       "      <td id=\"T_a18b1_row10_col4\" class=\"data row10 col4\" >23.15</td>\n",
       "      <td id=\"T_a18b1_row10_col5\" class=\"data row10 col5\" >29.44</td>\n",
       "      <td id=\"T_a18b1_row10_col6\" class=\"data row10 col6\" >100.00</td>\n",
       "      <td id=\"T_a18b1_row10_col7\" class=\"data row10 col7\" >18.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a18b1_level0_row11\" class=\"row_heading level0 row11\" >greedy_dropout</th>\n",
       "      <td id=\"T_a18b1_row11_col0\" class=\"data row11 col0\" >20.68</td>\n",
       "      <td id=\"T_a18b1_row11_col1\" class=\"data row11 col1\" >19.51</td>\n",
       "      <td id=\"T_a18b1_row11_col2\" class=\"data row11 col2\" >20.72</td>\n",
       "      <td id=\"T_a18b1_row11_col3\" class=\"data row11 col3\" >16.58</td>\n",
       "      <td id=\"T_a18b1_row11_col4\" class=\"data row11 col4\" >20.29</td>\n",
       "      <td id=\"T_a18b1_row11_col5\" class=\"data row11 col5\" >24.79</td>\n",
       "      <td id=\"T_a18b1_row11_col6\" class=\"data row11 col6\" >100.00</td>\n",
       "      <td id=\"T_a18b1_row11_col7\" class=\"data row11 col7\" >14.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fee67b40b80>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bleu_scorer = BLEU()\n",
    "\n",
    "sel_ref_bleu = {\n",
    "    selector: {\n",
    "        sampler: bleu_scorer.corpus_score(sampled, [[t for t in smpl.ref]]).score\n",
    "        for sampler, sampled in by_sampler.items()\n",
    "    }\n",
    "    for selector, by_sampler in selections.items()\n",
    "}\n",
    "map_bleu = pd.DataFrame(sel_ref_bleu)\n",
    "map_bleu.style.background_gradient(axis=None).format(precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "57c4238c",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_gens = [\n",
    "    'default', 'beam_search', 'sampling', 'sampling_p08',\n",
    "    'beam_dbs_1', 'beam_dbs_3', 'beam_dbs_10', \n",
    "    'beam_diversity_1', 'beam_diversity_3', 'beam_diversity_10',\n",
    "    'greedy_dropout', 'beam_dropout',\n",
    "]\n",
    "final_gens_names = [\n",
    "    'Default', 'Beam search', 'Sampling', 'Sampling P=80', \n",
    "    'DBS_1', 'DBS_3', 'DBS_10',\n",
    "    'D_Dec_1', 'D_Dec_3', 'D_Dec_10',\n",
    "    'MC gready', 'MC beam',\n",
    "]\n",
    "\n",
    "titles = ['XNLI', 'LaBSE', 'COMET', 'BLEU']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1329181b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "for i, the_map in enumerate([map_xnli, map_labse, map_comet, map_bleu]):    \n",
    "    ax = plt.subplot(1, 4, i+1)\n",
    "    im = ax.imshow(the_map.loc[final_gens, final_rankers], cmap='Greens')\n",
    "    cbar = ax.figure.colorbar(im, ax=ax)\n",
    "    \n",
    "    ax.set_xticks(np.arange(len(final_rankers_names)), labels=final_rankers_names)\n",
    "    if i == 0:\n",
    "        ax.set_yticks(np.arange(len(final_gens_names)), labels=final_gens_names)\n",
    "    else:\n",
    "        ax.set_yticks([])\n",
    "        ax.tick_params(left=False)\n",
    "    ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)\n",
    "    plt.setp(ax.get_xticklabels(), rotation=90)\n",
    "    \n",
    "    plt.title(titles[i], y=-0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5efeb457",
   "metadata": {},
   "source": [
    "### Table 2: average COMET scores by initial pahology group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fdff9ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "methods = ['COMET-QE', 'ALTI_avg_sc', 'LASER2', 'LABSE', 'XNLI']\n",
    "method_names = ['No reranking', 'COMET-QE', 'ALTI', 'LASER', 'LaBSE', 'XNLI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fb0364a2",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_38b93_level0_col0\" class=\"col_heading level0 col0\" >Fully detached</th>\n",
       "      <th id=\"T_38b93_level0_col1\" class=\"col_heading level0 col1\" >Strongly detached</th>\n",
       "      <th id=\"T_38b93_level0_col2\" class=\"col_heading level0 col2\" >Other errors</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th id=\"T_38b93_level0_row0\" class=\"row_heading level0 row0\" >No reranking</th>\n",
       "      <td id=\"T_38b93_row0_col0\" class=\"data row0 col0\" >-1.23</td>\n",
       "      <td id=\"T_38b93_row0_col1\" class=\"data row0 col1\" >-0.97</td>\n",
       "      <td id=\"T_38b93_row0_col2\" class=\"data row0 col2\" >-0.59</td>\n",
       "      <td id=\"T_38b93_row0_col3\" class=\"data row0 col3\" >0.27</td>\n",
       "      <td id=\"T_38b93_row0_col4\" class=\"data row0 col4\" >-0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_38b93_level0_row1\" class=\"row_heading level0 row1\" >COMET-QE</th>\n",
       "      <td id=\"T_38b93_row1_col0\" class=\"data row1 col0\" >-0.21</td>\n",
       "      <td id=\"T_38b93_row1_col1\" class=\"data row1 col1\" >-0.13</td>\n",
       "      <td id=\"T_38b93_row1_col2\" class=\"data row1 col2\" >-0.14</td>\n",
       "      <td id=\"T_38b93_row1_col3\" class=\"data row1 col3\" >0.35</td>\n",
       "      <td id=\"T_38b93_row1_col4\" class=\"data row1 col4\" >-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_38b93_level0_row2\" class=\"row_heading level0 row2\" >ALTI</th>\n",
       "      <td id=\"T_38b93_row2_col0\" class=\"data row2 col0\" >-0.17</td>\n",
       "      <td id=\"T_38b93_row2_col1\" class=\"data row2 col1\" >-0.24</td>\n",
       "      <td id=\"T_38b93_row2_col2\" class=\"data row2 col2\" >-0.39</td>\n",
       "      <td id=\"T_38b93_row2_col3\" class=\"data row2 col3\" >0.25</td>\n",
       "      <td id=\"T_38b93_row2_col4\" class=\"data row2 col4\" >-0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_38b93_level0_row3\" class=\"row_heading level0 row3\" >LASER</th>\n",
       "      <td id=\"T_38b93_row3_col0\" class=\"data row3 col0\" >-0.11</td>\n",
       "      <td id=\"T_38b93_row3_col1\" class=\"data row3 col1\" >-0.23</td>\n",
       "      <td id=\"T_38b93_row3_col2\" class=\"data row3 col2\" >-0.35</td>\n",
       "      <td id=\"T_38b93_row3_col3\" class=\"data row3 col3\" >0.27</td>\n",
       "      <td id=\"T_38b93_row3_col4\" class=\"data row3 col4\" >-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_38b93_level0_row4\" class=\"row_heading level0 row4\" >LaBSE</th>\n",
       "      <td id=\"T_38b93_row4_col0\" class=\"data row4 col0\" >-0.07</td>\n",
       "      <td id=\"T_38b93_row4_col1\" class=\"data row4 col1\" >-0.12</td>\n",
       "      <td id=\"T_38b93_row4_col2\" class=\"data row4 col2\" >-0.26</td>\n",
       "      <td id=\"T_38b93_row4_col3\" class=\"data row4 col3\" >0.39</td>\n",
       "      <td id=\"T_38b93_row4_col4\" class=\"data row4 col4\" >-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_38b93_level0_row5\" class=\"row_heading level0 row5\" >XNLI</th>\n",
       "      <td id=\"T_38b93_row5_col0\" class=\"data row5 col0\" >-0.12</td>\n",
       "      <td id=\"T_38b93_row5_col1\" class=\"data row5 col1\" >-0.18</td>\n",
       "      <td id=\"T_38b93_row5_col2\" class=\"data row5 col2\" >-0.28</td>\n",
       "      <td id=\"T_38b93_row5_col3\" class=\"data row5 col3\" >0.30</td>\n",
       "      <td id=\"T_38b93_row5_col4\" class=\"data row5 col4\" >-0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fee6abf96d0>"
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smpl2 = smpl.copy()\n",
    "smpl2['baseline'] = pd.Series(sel_comet_raw['first']['default'], index=smpl.index)\n",
    "for score_method in methods:\n",
    "    smpl2[score_method] = sel_comet_raw[score_method]['beam_dropout']\n",
    "\n",
    "table = smpl2.groupby('error_class').mean(numeric_only=True).iloc[:, 10:].T\n",
    "table.index = method_names\n",
    "table = table[[3, 2, 1, 0]]\n",
    "table.columns = ['Fully detached', 'Strongly detached', 'Other errors', 'Correct']\n",
    "table['Average'] = table.mean(axis=1)\n",
    "table.style.background_gradient(axis=None).format(precision=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb2f087",
   "metadata": {},
   "source": [
    "### Table 3:  average XNLI scores by initial pahology group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4799a4f3",
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "#T_b18e4_row5_col2 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b18e4_row5_col3 {\n",
       "  background-color: #023b5d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b18e4_row5_col4 {\n",
       "  background-color: #045483;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_b18e4\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b18e4_level0_col0\" class=\"col_heading level0 col0\" >Fully detached</th>\n",
       "      <th id=\"T_b18e4_level0_col1\" class=\"col_heading level0 col1\" >Strongly detached</th>\n",
       "      <th id=\"T_b18e4_level0_col2\" class=\"col_heading level0 col2\" >Other errors</th>\n",
       "      <th id=\"T_b18e4_level0_col3\" class=\"col_heading level0 col3\" >Correct</th>\n",
       "      <th id=\"T_b18e4_level0_col4\" class=\"col_heading level0 col4\" >Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b18e4_level0_row0\" class=\"row_heading level0 row0\" >No reranking</th>\n",
       "      <td id=\"T_b18e4_row0_col0\" class=\"data row0 col0\" >2.16</td>\n",
       "      <td id=\"T_b18e4_row0_col1\" class=\"data row0 col1\" >29.79</td>\n",
       "      <td id=\"T_b18e4_row0_col2\" class=\"data row0 col2\" >80.25</td>\n",
       "      <td id=\"T_b18e4_row0_col3\" class=\"data row0 col3\" >92.85</td>\n",
       "      <td id=\"T_b18e4_row0_col4\" class=\"data row0 col4\" >51.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b18e4_level0_row1\" class=\"row_heading level0 row1\" >COMET-QE</th>\n",
       "      <td id=\"T_b18e4_row1_col0\" class=\"data row1 col0\" >59.49</td>\n",
       "      <td id=\"T_b18e4_row1_col1\" class=\"data row1 col1\" >69.33</td>\n",
       "      <td id=\"T_b18e4_row1_col2\" class=\"data row1 col2\" >84.78</td>\n",
       "      <td id=\"T_b18e4_row1_col3\" class=\"data row1 col3\" >92.76</td>\n",
       "      <td id=\"T_b18e4_row1_col4\" class=\"data row1 col4\" >76.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b18e4_level0_row2\" class=\"row_heading level0 row2\" >ALTI</th>\n",
       "      <td id=\"T_b18e4_row2_col0\" class=\"data row2 col0\" >63.80</td>\n",
       "      <td id=\"T_b18e4_row2_col1\" class=\"data row2 col1\" >73.02</td>\n",
       "      <td id=\"T_b18e4_row2_col2\" class=\"data row2 col2\" >92.26</td>\n",
       "      <td id=\"T_b18e4_row2_col3\" class=\"data row2 col3\" >91.10</td>\n",
       "      <td id=\"T_b18e4_row2_col4\" class=\"data row2 col4\" >80.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b18e4_level0_row3\" class=\"row_heading level0 row3\" >LASER</th>\n",
       "      <td id=\"T_b18e4_row3_col0\" class=\"data row3 col0\" >71.60</td>\n",
       "      <td id=\"T_b18e4_row3_col1\" class=\"data row3 col1\" >72.95</td>\n",
       "      <td id=\"T_b18e4_row3_col2\" class=\"data row3 col2\" >92.20</td>\n",
       "      <td id=\"T_b18e4_row3_col3\" class=\"data row3 col3\" >91.99</td>\n",
       "      <td id=\"T_b18e4_row3_col4\" class=\"data row3 col4\" >82.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b18e4_level0_row4\" class=\"row_heading level0 row4\" >LaBSE</th>\n",
       "      <td id=\"T_b18e4_row4_col0\" class=\"data row4 col0\" >73.61</td>\n",
       "      <td id=\"T_b18e4_row4_col1\" class=\"data row4 col1\" >79.64</td>\n",
       "      <td id=\"T_b18e4_row4_col2\" class=\"data row4 col2\" >92.06</td>\n",
       "      <td id=\"T_b18e4_row4_col3\" class=\"data row4 col3\" >94.44</td>\n",
       "      <td id=\"T_b18e4_row4_col4\" class=\"data row4 col4\" >84.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b18e4_level0_row5\" class=\"row_heading level0 row5\" >XNLI</th>\n",
       "      <td id=\"T_b18e4_row5_col0\" class=\"data row5 col0\" >75.26</td>\n",
       "      <td id=\"T_b18e4_row5_col1\" class=\"data row5 col1\" >82.58</td>\n",
       "      <td id=\"T_b18e4_row5_col2\" class=\"data row5 col2\" >98.01</td>\n",
       "      <td id=\"T_b18e4_row5_col3\" class=\"data row5 col3\" >96.87</td>\n",
       "      <td id=\"T_b18e4_row5_col4\" class=\"data row5 col4\" >88.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fee6add1ee0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smpl2 = smpl.copy()\n",
    "smpl2['baseline'] = pd.Series(sel_src_nli_raw['first']['default'], index=smpl.index)\n",
    "for score_method in methods:\n",
    "    smpl2[score_method] = sel_src_nli_raw[score_method]['beam_dropout']\n",
    "\n",
    "table = smpl2.groupby('error_class').mean(numeric_only=True).iloc[:, 10:].T * 100\n",
    "table.index = method_names\n",
    "table = table[[3, 2, 1, 0]]\n",
    "table.columns = ['Fully detached', 'Strongly detached', 'Other errors', 'Correct']\n",
    "table['Average'] = table.mean(axis=1)\n",
    "table.style.background_gradient(axis=None).format(precision=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c8cc89",
   "metadata": {},
   "source": [
    "# Effect of the number of hypotheses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "93236a17",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../computed_data/diverse-decoding-results-more-hypotheses.json', 'r') as f:\n",
    "    unpacked_long = json.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "622aac7a",
   "metadata": {},
   "source": [
    "### Figure 6: COMET scores for each generation method and number of hypotheses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2c387817",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_ids = {}\n",
    "\n",
    "for k in range(1, 51):\n",
    "    selected_ids[k] = {\n",
    "        score_method: {\n",
    "            gen_method: [\n",
    "                np.argmax(unpacked_long['candidate_scores'][score_method][gen_method][i][:k])\n",
    "                for i, hyps in enumerate(hyps_list)\n",
    "            ]\n",
    "            for gen_method, hyps_list in unpacked_long['candidates'].items()\n",
    "        } \n",
    "        for score_method in unpacked_long['candidate_scores']\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "15f838e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select by XNLI, then score by Comet\n",
    "scores = pd.DataFrame({\n",
    "    k: {\n",
    "        gen_method: np.mean([\n",
    "            hyp_scores[hyp_id]\n",
    "            for hyp_scores, hyp_id in zip(unpacked_long['candidate_scores']['COMET'][gen_method], chosen_ids) \n",
    "        ]) #np.mean(chosen_ids)\n",
    "        for gen_method, chosen_ids in selected_for_size['XNLI'].items()\n",
    "    }\n",
    "    for k, selected_for_size in selected_ids.items()\n",
    "}).T\n",
    "\n",
    "scores.plot(\n",
    "    xlabel='number of hypotheses', \n",
    "    ylabel='average COMET score',\n",
    "    #title='mean best XNLI scores by generation method',\n",
    ").legend(loc=(1.1, 0));\n",
    "plt.title('Average COMET score for selecting with XNLI from top N hypotheses.');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7a995d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select by XNLI, then score by Comet\n",
    "scores = pd.DataFrame({\n",
    "    k: {\n",
    "        gen_method: np.mean([\n",
    "            hyp_scores[hyp_id]\n",
    "            for hyp_scores, hyp_id in zip(unpacked_long['candidate_scores']['COMET'][gen_method], chosen_ids) \n",
    "        ]) #np.mean(chosen_ids)\n",
    "        for gen_method, chosen_ids in selected_for_size['COMET-QE'].items()\n",
    "    }\n",
    "    for k, selected_for_size in selected_ids.items()\n",
    "}).T\n",
    "\n",
    "scores.plot(\n",
    "    xlabel='number of hypotheses', \n",
    "    ylabel='average COMET score',\n",
    "    #title='mean best XNLI scores by generation method',\n",
    ").legend(loc=(1.1, 0));\n",
    "plt.title('Average COMET score for selecting with COMET-QE from top N hypotheses.');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "84767fcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select by XNLI, then score by Comet\n",
    "scores = pd.DataFrame({\n",
    "    k: {\n",
    "        gen_method: np.mean([\n",
    "            hyp_scores[hyp_id]\n",
    "            for hyp_scores, hyp_id in zip(unpacked_long['candidate_scores']['COMET'][gen_method], chosen_ids) \n",
    "        ]) #np.mean(chosen_ids)\n",
    "        for gen_method, chosen_ids in selected_for_size['LABSE'].items()\n",
    "    }\n",
    "    for k, selected_for_size in selected_ids.items()\n",
    "}).T\n",
    "\n",
    "scores.plot(\n",
    "    xlabel='number of hypotheses', \n",
    "    ylabel='average COMET score',\n",
    "    #title='mean best XNLI scores by generation method',\n",
    ").legend(loc=(1.1, 0));\n",
    "plt.title('Average COMET score for selecting with LaBSE from top N hypotheses.');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f91456c0",
   "metadata": {},
   "source": [
    "# Human annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eb510a9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "stacked_annotations = pd.read_csv('../annotated_data/annotate_hallucination_mitigation_v7_stacked.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "da42ed6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "annotator_1    201\n",
       "annotator_2    201\n",
       "annotator_3    201\n",
       "Name: annotator, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked_annotations.annotator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b7870aa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_to_data = {name: data.reset_index(drop=True) for name, data in stacked_annotations.groupby('annotator')}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cc16bce0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['annotator_1', 'annotator_2', 'annotator_3'])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_to_data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e972eed",
   "metadata": {},
   "source": [
    "### Compute agreement "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3e3625",
   "metadata": {},
   "source": [
    "Extract lists of labels for each item from each annotator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fa30a9fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_colnames = [f'label{i}' for i in range(4)]\n",
    "labelled_long = {k: v[label_colnames].unstack() for k, v in name_to_data.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b45d8860",
   "metadata": {},
   "source": [
    "Compute label counts for each item and inter-annotator agreement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9865398e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(602, 5)\n",
      "0.5459463239444357\n"
     ]
    }
   ],
   "source": [
    "t2, labels = aggregate_raters(np.stack([v.dropna().values for v in labelled_long.values()], axis=1))\n",
    "\n",
    "print(t2.shape)\n",
    "print(fleiss_kappa(t2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da97c9f8",
   "metadata": {},
   "source": [
    "Compute ageement for coarse-grained labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dcb84f25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(602, 3)\n",
      "0.566360827351435\n"
     ]
    }
   ],
   "source": [
    "t3 = pd.DataFrame(t2, columns=labels)\n",
    "t4 = pd.DataFrame({\n",
    "    'Hallucination': t3['FULL_H'] + t3['PART_H'],\n",
    "    'Error': t3['UNDER'] + t3['OTHER'],\n",
    "    'OK': t3['OK'],\n",
    "})\n",
    "\n",
    "print(t4.shape)\n",
    "print(fleiss_kappa(t4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "547f46fa",
   "metadata": {},
   "source": [
    "Count ties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7a658d4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "print(sum((t4.max(1)==1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16f24c36",
   "metadata": {},
   "source": [
    "### Figure 7: Percentages of translation pathologies before and after reranking"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26595ad9",
   "metadata": {},
   "source": [
    "Undo deduplication of the translations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "21eaeaf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "tran_names = [f'mt{i}' for i in range(4)]\n",
    "all_systems = ['default', 'alti', 'labse', 'comet']\n",
    "\n",
    "for k, v in name_to_data.items():\n",
    "    for system in all_systems:\n",
    "        eq = (v[f'mt_{system}'] == v[tran_names].T).T\n",
    "        assert (eq.sum(1) == 1).all()\n",
    "        v[f'label_{system}'] = v[label_colnames].values[eq.values]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a017c9",
   "metadata": {},
   "source": [
    "Create a long table of labels on the text+system level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "053fcae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "long_joint = []\n",
    "for k, v in name_to_data.items():\n",
    "    tmp = v[[f'label_{system}' for system in all_systems]].unstack().reset_index()\n",
    "    tmp.columns = ['method', 'orig_id', 'label']\n",
    "    tmp['who'] = k\n",
    "    long_joint.append(tmp)\n",
    "\n",
    "long_joint = pd.concat(long_joint)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef37bca",
   "metadata": {},
   "source": [
    "Aggregate the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7f29c925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>orig_id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>label_alti</td>\n",
       "      <td>0</td>\n",
       "      <td>Hallucination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>label_alti</td>\n",
       "      <td>1</td>\n",
       "      <td>Hallucination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>label_alti</td>\n",
       "      <td>2</td>\n",
       "      <td>Hallucination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>label_alti</td>\n",
       "      <td>3</td>\n",
       "      <td>Correct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>label_alti</td>\n",
       "      <td>4</td>\n",
       "      <td>Hallucination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>label_labse</td>\n",
       "      <td>196</td>\n",
       "      <td>Error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>label_labse</td>\n",
       "      <td>197</td>\n",
       "      <td>Hallucination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>label_labse</td>\n",
       "      <td>198</td>\n",
       "      <td>Correct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>label_labse</td>\n",
       "      <td>199</td>\n",
       "      <td>Error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>label_labse</td>\n",
       "      <td>200</td>\n",
       "      <td>Correct</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>804 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          method  orig_id          label\n",
       "0     label_alti        0  Hallucination\n",
       "1     label_alti        1  Hallucination\n",
       "2     label_alti        2  Hallucination\n",
       "3     label_alti        3        Correct\n",
       "4     label_alti        4  Hallucination\n",
       "..           ...      ...            ...\n",
       "799  label_labse      196          Error\n",
       "800  label_labse      197  Hallucination\n",
       "801  label_labse      198        Correct\n",
       "802  label_labse      199          Error\n",
       "803  label_labse      200        Correct\n",
       "\n",
       "[804 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "REMAP = {'FULL_H': 'Hallucination',  'PART_H': 'Hallucination', 'UNDER':'Error', 'OTHER':'Error', 'OK': 'Correct'}\n",
    "PRIORITY2 = ['Hallucination',  'Error', 'Correct']\n",
    "\n",
    "\n",
    "def choose_mode2(series):\n",
    "    \"\"\" Return the most frequent value, and the most pessimistic value in case of ties.\"\"\"\n",
    "    vc = series.apply(lambda x: REMAP[x]).value_counts()\n",
    "    if len(vc) == 1 or vc[0] > vc[1]:\n",
    "        return vc.index[0]\n",
    "    # tie resolution\n",
    "    # print(dict(vc))\n",
    "    top_labels = set(vc[vc==vc.max()].index)\n",
    "    for label in PRIORITY2:\n",
    "        if label in top_labels:\n",
    "            return label\n",
    "\n",
    "modes = long_joint.dropna().groupby(['method', 'orig_id']).label.apply(choose_mode2).reset_index()\n",
    "modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6153efb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>label</th>\n",
       "      <th>Correct</th>\n",
       "      <th>Error</th>\n",
       "      <th>Hallucination</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LaBSE</th>\n",
       "      <td>112</td>\n",
       "      <td>56</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comet-QE</th>\n",
       "      <td>109</td>\n",
       "      <td>47</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALTI</th>\n",
       "      <td>99</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Before reranking</th>\n",
       "      <td>40</td>\n",
       "      <td>54</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "label             Correct  Error  Hallucination\n",
       "LaBSE                 112     56             33\n",
       "Comet-QE              109     47             45\n",
       "ALTI                   99     57             45\n",
       "Before reranking       40     54            107"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct = pd.crosstab(modes.method, modes.label).sort_values('Correct', ascending=False)\n",
    "ct.index = [x[6:] for x in ct.index]\n",
    "ct.rename(\n",
    "    {'labse': 'LaBSE', 'comet': 'Comet-QE', 'alti': 'ALTI', 'default': 'Before reranking'}, \n",
    "    axis=0, inplace=True,\n",
    ")\n",
    "ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "dddd83a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_2f4df\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >label</th>\n",
       "      <th id=\"T_2f4df_level0_col0\" class=\"col_heading level0 col0\" >Correct</th>\n",
       "      <th id=\"T_2f4df_level0_col1\" class=\"col_heading level0 col1\" >Error</th>\n",
       "      <th id=\"T_2f4df_level0_col2\" class=\"col_heading level0 col2\" >Hallucination</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2f4df_level0_row0\" class=\"row_heading level0 row0\" >LaBSE</th>\n",
       "      <td id=\"T_2f4df_row0_col0\" class=\"data row0 col0\" >0.56</td>\n",
       "      <td id=\"T_2f4df_row0_col1\" class=\"data row0 col1\" >0.28</td>\n",
       "      <td id=\"T_2f4df_row0_col2\" class=\"data row0 col2\" >0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2f4df_level0_row1\" class=\"row_heading level0 row1\" >Comet-QE</th>\n",
       "      <td id=\"T_2f4df_row1_col0\" class=\"data row1 col0\" >0.54</td>\n",
       "      <td id=\"T_2f4df_row1_col1\" class=\"data row1 col1\" >0.23</td>\n",
       "      <td id=\"T_2f4df_row1_col2\" class=\"data row1 col2\" >0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2f4df_level0_row2\" class=\"row_heading level0 row2\" >ALTI</th>\n",
       "      <td id=\"T_2f4df_row2_col0\" class=\"data row2 col0\" >0.49</td>\n",
       "      <td id=\"T_2f4df_row2_col1\" class=\"data row2 col1\" >0.28</td>\n",
       "      <td id=\"T_2f4df_row2_col2\" class=\"data row2 col2\" >0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2f4df_level0_row3\" class=\"row_heading level0 row3\" >Before reranking</th>\n",
       "      <td id=\"T_2f4df_row3_col0\" class=\"data row3 col0\" >0.20</td>\n",
       "      <td id=\"T_2f4df_row3_col1\" class=\"data row3 col1\" >0.27</td>\n",
       "      <td id=\"T_2f4df_row3_col2\" class=\"data row3 col2\" >0.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fee68021c70>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct_norm = (ct.T / ct.sum(1)).T\n",
    "ct_norm.style.format(precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9f4ac497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = ct_norm.loc[\n",
    "    ['Before reranking', 'ALTI', 'Comet-QE', 'LaBSE'][::-1],\n",
    "    ['Correct', 'Error', 'Hallucination'][::-1],\n",
    "]\n",
    "ax = df.plot(kind='barh', stacked=True, legend=None, color=['red', 'orange', 'green'])\n",
    "plt.legend(loc=(1.05, 0.8))\n",
    "\n",
    "#for container in ax.containers:\n",
    "#    ax.bar_label(container)\n",
    "\n",
    "labels = [f'{i:.0%}' for i in df.to_numpy().flatten(order='F')]\n",
    "\n",
    "for i, patch in enumerate(ax.patches):\n",
    "    x, y = patch.get_xy()\n",
    "    x += patch.get_width() / 2\n",
    "    y += patch.get_height() / 2\n",
    "    ax.annotate(labels[i], (x, y), ha='center', va='center', c='white')\n",
    "\n",
    "ax.set_xticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "273da59a",
   "metadata": {},
   "source": [
    "### Tables 5 and 6: Statistical significance of the differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b7f1cab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_names = {'labse': 'LaBSE', 'comet': 'Comet-QE', 'alti': 'ALTI', 'default': 'No reranking'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6d51d3ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_ce3da\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ce3da_level0_col0\" class=\"col_heading level0 col0\" >Method 1</th>\n",
       "      <th id=\"T_ce3da_level0_col1\" class=\"col_heading level0 col1\" >Method 2</th>\n",
       "      <th id=\"T_ce3da_level0_col2\" class=\"col_heading level0 col2\" >Rate 1</th>\n",
       "      <th id=\"T_ce3da_level0_col3\" class=\"col_heading level0 col3\" >Rate 2</th>\n",
       "      <th id=\"T_ce3da_level0_col4\" class=\"col_heading level0 col4\" >p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ce3da_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_ce3da_row0_col0\" class=\"data row0 col0\" >LaBSE</td>\n",
       "      <td id=\"T_ce3da_row0_col1\" class=\"data row0 col1\" >Comet-QE</td>\n",
       "      <td id=\"T_ce3da_row0_col2\" class=\"data row0 col2\" >0.56</td>\n",
       "      <td id=\"T_ce3da_row0_col3\" class=\"data row0 col3\" >0.54</td>\n",
       "      <td id=\"T_ce3da_row0_col4\" class=\"data row0 col4\" >0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce3da_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_ce3da_row1_col0\" class=\"data row1 col0\" >LaBSE</td>\n",
       "      <td id=\"T_ce3da_row1_col1\" class=\"data row1 col1\" >ALTI</td>\n",
       "      <td id=\"T_ce3da_row1_col2\" class=\"data row1 col2\" >0.56</td>\n",
       "      <td id=\"T_ce3da_row1_col3\" class=\"data row1 col3\" >0.49</td>\n",
       "      <td id=\"T_ce3da_row1_col4\" class=\"data row1 col4\" >0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce3da_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_ce3da_row2_col0\" class=\"data row2 col0\" >LaBSE</td>\n",
       "      <td id=\"T_ce3da_row2_col1\" class=\"data row2 col1\" >No reranking</td>\n",
       "      <td id=\"T_ce3da_row2_col2\" class=\"data row2 col2\" >0.56</td>\n",
       "      <td id=\"T_ce3da_row2_col3\" class=\"data row2 col3\" >0.20</td>\n",
       "      <td id=\"T_ce3da_row2_col4\" class=\"data row2 col4\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce3da_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_ce3da_row3_col0\" class=\"data row3 col0\" >Comet-QE</td>\n",
       "      <td id=\"T_ce3da_row3_col1\" class=\"data row3 col1\" >ALTI</td>\n",
       "      <td id=\"T_ce3da_row3_col2\" class=\"data row3 col2\" >0.54</td>\n",
       "      <td id=\"T_ce3da_row3_col3\" class=\"data row3 col3\" >0.49</td>\n",
       "      <td id=\"T_ce3da_row3_col4\" class=\"data row3 col4\" >0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce3da_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_ce3da_row4_col0\" class=\"data row4 col0\" >Comet-QE</td>\n",
       "      <td id=\"T_ce3da_row4_col1\" class=\"data row4 col1\" >No reranking</td>\n",
       "      <td id=\"T_ce3da_row4_col2\" class=\"data row4 col2\" >0.54</td>\n",
       "      <td id=\"T_ce3da_row4_col3\" class=\"data row4 col3\" >0.20</td>\n",
       "      <td id=\"T_ce3da_row4_col4\" class=\"data row4 col4\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce3da_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_ce3da_row5_col0\" class=\"data row5 col0\" >ALTI</td>\n",
       "      <td id=\"T_ce3da_row5_col1\" class=\"data row5 col1\" >No reranking</td>\n",
       "      <td id=\"T_ce3da_row5_col2\" class=\"data row5 col2\" >0.49</td>\n",
       "      <td id=\"T_ce3da_row5_col3\" class=\"data row5 col3\" >0.20</td>\n",
       "      <td id=\"T_ce3da_row5_col4\" class=\"data row5 col4\" >0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fee67b02ac0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct = pd.crosstab(modes.method, modes.label).sort_values('Correct', ascending=False)\n",
    "diffs = []\n",
    "\n",
    "for i, m1 in enumerate(ct.index):\n",
    "    for m2 in ct.index[i+1:]:\n",
    "        x1 = (modes[modes.method==m1].label == 'Correct').astype(int)\n",
    "        x2 = (modes[modes.method==m2].label == 'Correct').astype(int)\n",
    "        res = ttest_rel(x1, x2)\n",
    "        diffs.append({\n",
    "            'Method 1': method_names[m1[6:]], 'Method 2': method_names[m2[6:]], \n",
    "            'Rate 1': x1.mean(), 'Rate 2': x2.mean(), \n",
    "            'p-value': res.pvalue,\n",
    "        })\n",
    "diffs = pd.DataFrame(diffs)\n",
    "diffs.style.format(precision=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a87686d",
   "metadata": {},
   "source": [
    "The same is true for comparing hallucination rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e8ad8fc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_8f6f9\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8f6f9_level0_col0\" class=\"col_heading level0 col0\" >Method 1</th>\n",
       "      <th id=\"T_8f6f9_level0_col1\" class=\"col_heading level0 col1\" >Method 2</th>\n",
       "      <th id=\"T_8f6f9_level0_col2\" class=\"col_heading level0 col2\" >Rate 1</th>\n",
       "      <th id=\"T_8f6f9_level0_col3\" class=\"col_heading level0 col3\" >Rate 2</th>\n",
       "      <th id=\"T_8f6f9_level0_col4\" class=\"col_heading level0 col4\" >p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8f6f9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_8f6f9_row0_col0\" class=\"data row0 col0\" >LaBSE</td>\n",
       "      <td id=\"T_8f6f9_row0_col1\" class=\"data row0 col1\" >Comet-QE</td>\n",
       "      <td id=\"T_8f6f9_row0_col2\" class=\"data row0 col2\" >0.16</td>\n",
       "      <td id=\"T_8f6f9_row0_col3\" class=\"data row0 col3\" >0.22</td>\n",
       "      <td id=\"T_8f6f9_row0_col4\" class=\"data row0 col4\" >0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8f6f9_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_8f6f9_row1_col0\" class=\"data row1 col0\" >LaBSE</td>\n",
       "      <td id=\"T_8f6f9_row1_col1\" class=\"data row1 col1\" >ALTI</td>\n",
       "      <td id=\"T_8f6f9_row1_col2\" class=\"data row1 col2\" >0.16</td>\n",
       "      <td id=\"T_8f6f9_row1_col3\" class=\"data row1 col3\" >0.22</td>\n",
       "      <td id=\"T_8f6f9_row1_col4\" class=\"data row1 col4\" >0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8f6f9_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_8f6f9_row2_col0\" class=\"data row2 col0\" >LaBSE</td>\n",
       "      <td id=\"T_8f6f9_row2_col1\" class=\"data row2 col1\" >No reranking</td>\n",
       "      <td id=\"T_8f6f9_row2_col2\" class=\"data row2 col2\" >0.16</td>\n",
       "      <td id=\"T_8f6f9_row2_col3\" class=\"data row2 col3\" >0.53</td>\n",
       "      <td id=\"T_8f6f9_row2_col4\" class=\"data row2 col4\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8f6f9_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_8f6f9_row3_col0\" class=\"data row3 col0\" >Comet-QE</td>\n",
       "      <td id=\"T_8f6f9_row3_col1\" class=\"data row3 col1\" >ALTI</td>\n",
       "      <td id=\"T_8f6f9_row3_col2\" class=\"data row3 col2\" >0.22</td>\n",
       "      <td id=\"T_8f6f9_row3_col3\" class=\"data row3 col3\" >0.22</td>\n",
       "      <td id=\"T_8f6f9_row3_col4\" class=\"data row3 col4\" >1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8f6f9_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_8f6f9_row4_col0\" class=\"data row4 col0\" >Comet-QE</td>\n",
       "      <td id=\"T_8f6f9_row4_col1\" class=\"data row4 col1\" >No reranking</td>\n",
       "      <td id=\"T_8f6f9_row4_col2\" class=\"data row4 col2\" >0.22</td>\n",
       "      <td id=\"T_8f6f9_row4_col3\" class=\"data row4 col3\" >0.53</td>\n",
       "      <td id=\"T_8f6f9_row4_col4\" class=\"data row4 col4\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8f6f9_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_8f6f9_row5_col0\" class=\"data row5 col0\" >ALTI</td>\n",
       "      <td id=\"T_8f6f9_row5_col1\" class=\"data row5 col1\" >No reranking</td>\n",
       "      <td id=\"T_8f6f9_row5_col2\" class=\"data row5 col2\" >0.22</td>\n",
       "      <td id=\"T_8f6f9_row5_col3\" class=\"data row5 col3\" >0.53</td>\n",
       "      <td id=\"T_8f6f9_row5_col4\" class=\"data row5 col4\" >0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fee6559c790>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diffs = []\n",
    "\n",
    "for i, m1 in enumerate(ct.index):\n",
    "    for m2 in ct.index[i+1:]:\n",
    "        x1 = (modes[modes.method==m1].label == 'Hallucination').astype(int)\n",
    "        x2 = (modes[modes.method==m2].label == 'Hallucination').astype(int)\n",
    "        res = ttest_rel(x1, x2)\n",
    "        diffs.append({\n",
    "            'Method 1': method_names[m1[6:]], 'Method 2': method_names[m2[6:]], \n",
    "            'Rate 1': x1.mean(), 'Rate 2': x2.mean(), \n",
    "            'p-value': res.pvalue,\n",
    "        })\n",
    "\n",
    "diffs = pd.DataFrame(diffs)\n",
    "diffs.style.format(precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ceced8ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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